Overview

Dataset statistics

Number of variables 11
Number of observations 583
Missing cells 4
Missing cells (%) 0.1%
Duplicate rows 13
Duplicate rows (%) 2.2%
Total size in memory 50.2 KiB
Average record size in memory 88.2 B

Variable types

Numeric 9
Categorical 2

Alerts

Dataset has 13 (2.2%) duplicate rows Duplicates
Total_Bilirubin is highly overall correlated with Direct_Bilirubin and 1 other fields High correlation
Direct_Bilirubin is highly overall correlated with Total_Bilirubin and 1 other fields High correlation
Alamine_Aminotransferase is highly overall correlated with Aspartate_Aminotransferase High correlation
Aspartate_Aminotransferase is highly overall correlated with Total_Bilirubin and 2 other fields High correlation
Total_Protiens is highly overall correlated with Albumin High correlation
Albumin is highly overall correlated with Total_Protiens and 1 other fields High correlation
Albumin_and_Globulin_Ratio is highly overall correlated with Albumin High correlation

Reproduction

Analysis started 2023-03-24 12:25:30.310062
Analysis finished 2023-03-24 12:25:39.308344
Duration 9 seconds
Software version pandas-profiling v3.6.6
Download configuration config.json

Variables

Age
Real number (ℝ)

Distinct 72
Distinct (%) 12.3%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 44.746141
Minimum 4
Maximum 90
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 4.7 KiB
2023-03-24T17:55:39.379897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum 4
5-th percentile 18
Q1 33
median 45
Q3 58
95-th percentile 72
Maximum 90
Range 86
Interquartile range (IQR) 25

Descriptive statistics

Standard deviation 16.189833
Coefficient of variation (CV) 0.36181519
Kurtosis -0.56006564
Mean 44.746141
Median Absolute Deviation (MAD) 12
Skewness -0.029385313
Sum 26087
Variance 262.1107
Monotonicity Not monotonic
2023-03-24T17:55:39.484818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
60 34
 
5.8%
45 25
 
4.3%
50 23
 
3.9%
42 21
 
3.6%
38 21
 
3.6%
32 20
 
3.4%
48 20
 
3.4%
55 18
 
3.1%
65 17
 
2.9%
40 17
 
2.9%
Other values (62) 367
63.0%
Value Count Frequency (%)
4 2
0.3%
6 1
 
0.2%
7 2
0.3%
8 1
 
0.2%
10 1
 
0.2%
11 1
 
0.2%
12 2
0.3%
13 4
0.7%
14 2
0.3%
15 1
 
0.2%
Value Count Frequency (%)
90 1
 
0.2%
85 1
 
0.2%
84 1
 
0.2%
78 1
 
0.2%
75 14
2.4%
74 4
 
0.7%
73 2
 
0.3%
72 8
1.4%
70 9
1.5%
69 2
 
0.3%

Gender
Categorical

Distinct 2
Distinct (%) 0.3%
Missing 0
Missing (%) 0.0%
Memory size 4.7 KiB
Male
441 
Female
142 

Length

Max length 6
Median length 4
Mean length 4.4871355
Min length 4

Characters and Unicode

Total characters 2616
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Female
2nd row Male
3rd row Male
4th row Male
5th row Male

Common Values

Value Count Frequency (%)
Male 441
75.6%
Female 142
 
24.4%

Length

2023-03-24T17:55:39.579146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-24T17:55:39.687121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Value Count Frequency (%)
male 441
75.6%
female 142
 
24.4%

Most occurring characters

Value Count Frequency (%)
e 725
27.7%
a 583
22.3%
l 583
22.3%
M 441
16.9%
F 142
 
5.4%
m 142
 
5.4%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 2033
77.7%
Uppercase Letter 583
 
22.3%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 725
35.7%
a 583
28.7%
l 583
28.7%
m 142
 
7.0%
Uppercase Letter
Value Count Frequency (%)
M 441
75.6%
F 142
 
24.4%

Most occurring scripts

Value Count Frequency (%)
Latin 2616
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 725
27.7%
a 583
22.3%
l 583
22.3%
M 441
16.9%
F 142
 
5.4%
m 142
 
5.4%

Most occurring blocks

Value Count Frequency (%)
ASCII 2616
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 725
27.7%
a 583
22.3%
l 583
22.3%
M 441
16.9%
F 142
 
5.4%
m 142
 
5.4%

Total_Bilirubin
Real number (ℝ)

Distinct 113
Distinct (%) 19.4%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 3.2987993
Minimum 0.4
Maximum 75
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 4.7 KiB
2023-03-24T17:55:39.769570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum 0.4
5-th percentile 0.6
Q1 0.8
median 1
Q3 2.6
95-th percentile 16.35
Maximum 75
Range 74.6
Interquartile range (IQR) 1.8

Descriptive statistics

Standard deviation 6.2095217
Coefficient of variation (CV) 1.8823581
Kurtosis 37.163792
Mean 3.2987993
Median Absolute Deviation (MAD) 0.3
Skewness 4.907474
Sum 1923.2
Variance 38.55816
Monotonicity Not monotonic
2023-03-24T17:55:39.885453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0.8 91
15.6%
0.7 77
 
13.2%
0.9 57
 
9.8%
0.6 46
 
7.9%
1 28
 
4.8%
1.1 19
 
3.3%
1.8 14
 
2.4%
1.4 13
 
2.2%
1.3 12
 
2.1%
1.7 11
 
1.9%
Other values (103) 215
36.9%
Value Count Frequency (%)
0.4 1
 
0.2%
0.5 5
 
0.9%
0.6 46
7.9%
0.7 77
13.2%
0.8 91
15.6%
0.9 57
9.8%
1 28
 
4.8%
1.1 19
 
3.3%
1.2 8
 
1.4%
1.3 12
 
2.1%
Value Count Frequency (%)
75 1
0.2%
42.8 1
0.2%
32.6 1
0.2%
30.8 1
0.2%
30.5 2
0.3%
27.7 1
0.2%
27.2 1
0.2%
26.3 1
0.2%
25 1
0.2%
23.3 1
0.2%

Direct_Bilirubin
Real number (ℝ)

Distinct 80
Distinct (%) 13.7%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 1.4861063
Minimum 0.1
Maximum 19.7
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 4.7 KiB
2023-03-24T17:55:39.991076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum 0.1
5-th percentile 0.1
Q1 0.2
median 0.3
Q3 1.3
95-th percentile 8.4
Maximum 19.7
Range 19.6
Interquartile range (IQR) 1.1

Descriptive statistics

Standard deviation 2.8084976
Coefficient of variation (CV) 1.8898362
Kurtosis 11.352529
Mean 1.4861063
Median Absolute Deviation (MAD) 0.2
Skewness 3.2124029
Sum 866.4
Variance 7.8876589
Monotonicity Not monotonic
2023-03-24T17:55:40.179522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0.2 194
33.3%
0.1 63
 
10.8%
0.3 51
 
8.7%
0.8 22
 
3.8%
0.4 21
 
3.6%
0.5 20
 
3.4%
0.6 16
 
2.7%
1 13
 
2.2%
1.3 12
 
2.1%
0.7 11
 
1.9%
Other values (70) 160
27.4%
Value Count Frequency (%)
0.1 63
 
10.8%
0.2 194
33.3%
0.3 51
 
8.7%
0.4 21
 
3.6%
0.5 20
 
3.4%
0.6 16
 
2.7%
0.7 11
 
1.9%
0.8 22
 
3.8%
0.9 7
 
1.2%
1 13
 
2.2%
Value Count Frequency (%)
19.7 1
0.2%
18.3 1
0.2%
17.1 1
0.2%
14.2 1
0.2%
14.1 1
0.2%
13.7 1
0.2%
12.8 1
0.2%
12.6 2
0.3%
12.1 1
0.2%
11.8 2
0.3%

Alkaline_Phosphotase
Real number (ℝ)

Distinct 263
Distinct (%) 45.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 290.57633
Minimum 63
Maximum 2110
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 4.7 KiB
2023-03-24T17:55:40.295451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum 63
5-th percentile 137
Q1 175.5
median 208
Q3 298
95-th percentile 698.1
Maximum 2110
Range 2047
Interquartile range (IQR) 122.5

Descriptive statistics

Standard deviation 242.93799
Coefficient of variation (CV) 0.83605568
Kurtosis 17.752828
Mean 290.57633
Median Absolute Deviation (MAD) 50
Skewness 3.7651064
Sum 169406
Variance 59018.867
Monotonicity Not monotonic
2023-03-24T17:55:40.405138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
198 11
 
1.9%
215 11
 
1.9%
298 11
 
1.9%
195 10
 
1.7%
190 10
 
1.7%
180 10
 
1.7%
145 9
 
1.5%
158 9
 
1.5%
182 9
 
1.5%
282 8
 
1.4%
Other values (253) 485
83.2%
Value Count Frequency (%)
63 1
0.2%
75 1
0.2%
90 1
0.2%
92 2
0.3%
97 1
0.2%
98 1
0.2%
100 2
0.3%
102 1
0.2%
103 1
0.2%
105 1
0.2%
Value Count Frequency (%)
2110 1
0.2%
1896 1
0.2%
1750 1
0.2%
1630 1
0.2%
1620 1
0.2%
1580 1
0.2%
1550 1
0.2%
1420 1
0.2%
1350 2
0.3%
1124 1
0.2%

Alamine_Aminotransferase
Real number (ℝ)

Distinct 152
Distinct (%) 26.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 80.713551
Minimum 10
Maximum 2000
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 4.7 KiB
2023-03-24T17:55:40.516231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum 10
5-th percentile 15
Q1 23
median 35
Q3 60.5
95-th percentile 232
Maximum 2000
Range 1990
Interquartile range (IQR) 37.5

Descriptive statistics

Standard deviation 182.62036
Coefficient of variation (CV) 2.2625737
Kurtosis 50.57945
Mean 80.713551
Median Absolute Deviation (MAD) 15
Skewness 6.5491919
Sum 47056
Variance 33350.194
Monotonicity Not monotonic
2023-03-24T17:55:40.611747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
25 25
 
4.3%
20 23
 
3.9%
22 18
 
3.1%
28 17
 
2.9%
21 17
 
2.9%
18 17
 
2.9%
30 15
 
2.6%
48 14
 
2.4%
15 14
 
2.4%
24 13
 
2.2%
Other values (142) 410
70.3%
Value Count Frequency (%)
10 4
 
0.7%
11 2
 
0.3%
12 10
1.7%
13 4
 
0.7%
14 8
1.4%
15 14
2.4%
16 8
1.4%
17 8
1.4%
18 17
2.9%
19 6
 
1.0%
Value Count Frequency (%)
2000 1
0.2%
1680 1
0.2%
1630 1
0.2%
1350 1
0.2%
1250 2
0.3%
950 1
0.2%
875 2
0.3%
790 1
0.2%
779 1
0.2%
622 1
0.2%

Aspartate_Aminotransferase
Real number (ℝ)

Distinct 177
Distinct (%) 30.4%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 109.91081
Minimum 10
Maximum 4929
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 4.7 KiB
2023-03-24T17:55:40.711977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum 10
5-th percentile 15.1
Q1 25
median 42
Q3 87
95-th percentile 400.9
Maximum 4929
Range 4919
Interquartile range (IQR) 62

Descriptive statistics

Standard deviation 288.91853
Coefficient of variation (CV) 2.6286635
Kurtosis 150.91988
Mean 109.91081
Median Absolute Deviation (MAD) 21
Skewness 10.546177
Sum 64078
Variance 83473.916
Monotonicity Not monotonic
2023-03-24T17:55:40.807729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
23 16
 
2.7%
30 14
 
2.4%
20 14
 
2.4%
21 14
 
2.4%
22 13
 
2.2%
28 13
 
2.2%
25 13
 
2.2%
34 12
 
2.1%
24 12
 
2.1%
32 12
 
2.1%
Other values (167) 450
77.2%
Value Count Frequency (%)
10 1
 
0.2%
11 2
 
0.3%
12 5
0.9%
13 3
 
0.5%
14 8
1.4%
15 11
1.9%
16 9
1.5%
17 8
1.4%
18 9
1.5%
19 11
1.9%
Value Count Frequency (%)
4929 1
 
0.2%
2946 1
 
0.2%
1600 1
 
0.2%
1500 1
 
0.2%
1050 2
0.3%
960 1
 
0.2%
950 1
 
0.2%
850 4
0.7%
844 1
 
0.2%
794 1
 
0.2%

Total_Protiens
Real number (ℝ)

Distinct 58
Distinct (%) 9.9%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 6.4831904
Minimum 2.7
Maximum 9.6
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 4.7 KiB
2023-03-24T17:55:40.916712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum 2.7
5-th percentile 4.61
Q1 5.8
median 6.6
Q3 7.2
95-th percentile 8.1
Maximum 9.6
Range 6.9
Interquartile range (IQR) 1.4

Descriptive statistics

Standard deviation 1.0854515
Coefficient of variation (CV) 0.16742551
Kurtosis 0.23303859
Mean 6.4831904
Median Absolute Deviation (MAD) 0.7
Skewness -0.28567219
Sum 3779.7
Variance 1.1782049
Monotonicity Not monotonic
2023-03-24T17:55:41.011613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
7 32
 
5.5%
6 30
 
5.1%
6.8 28
 
4.8%
6.9 25
 
4.3%
6.2 24
 
4.1%
7.1 22
 
3.8%
7.2 21
 
3.6%
8 20
 
3.4%
7.3 18
 
3.1%
5.6 18
 
3.1%
Other values (48) 345
59.2%
Value Count Frequency (%)
2.7 1
 
0.2%
2.8 1
 
0.2%
3 1
 
0.2%
3.6 3
0.5%
3.7 1
 
0.2%
3.8 2
0.3%
3.9 2
0.3%
4 2
0.3%
4.1 2
0.3%
4.3 3
0.5%
Value Count Frequency (%)
9.6 1
 
0.2%
9.5 1
 
0.2%
9.2 2
 
0.3%
8.9 1
 
0.2%
8.7 1
 
0.2%
8.6 3
 
0.5%
8.5 5
0.9%
8.4 3
 
0.5%
8.3 3
 
0.5%
8.2 8
1.4%

Albumin
Real number (ℝ)

Distinct 40
Distinct (%) 6.9%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 3.1418525
Minimum 0.9
Maximum 5.5
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 4.7 KiB
2023-03-24T17:55:41.122408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum 0.9
5-th percentile 1.8
Q1 2.6
median 3.1
Q3 3.8
95-th percentile 4.39
Maximum 5.5
Range 4.6
Interquartile range (IQR) 1.2

Descriptive statistics

Standard deviation 0.79551881
Coefficient of variation (CV) 0.25320056
Kurtosis -0.38790481
Mean 3.1418525
Median Absolute Deviation (MAD) 0.6
Skewness -0.043684729
Sum 1831.7
Variance 0.63285017
Monotonicity Not monotonic
2023-03-24T17:55:41.228570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
Value Count Frequency (%)
3 45
 
7.7%
4 37
 
6.3%
2.9 29
 
5.0%
3.1 28
 
4.8%
3.2 26
 
4.5%
3.9 25
 
4.3%
2.7 24
 
4.1%
2.5 24
 
4.1%
3.5 23
 
3.9%
2.6 21
 
3.6%
Other values (30) 301
51.6%
Value Count Frequency (%)
0.9 2
 
0.3%
1 1
 
0.2%
1.4 3
 
0.5%
1.5 3
 
0.5%
1.6 8
 
1.4%
1.7 3
 
0.5%
1.8 12
2.1%
1.9 7
 
1.2%
2 21
3.6%
2.1 14
2.4%
Value Count Frequency (%)
5.5 2
 
0.3%
5 1
 
0.2%
4.9 4
 
0.7%
4.8 2
 
0.3%
4.7 3
 
0.5%
4.6 4
 
0.7%
4.5 6
1.0%
4.4 8
1.4%
4.3 14
2.4%
4.2 12
2.1%

Albumin_and_Globulin_Ratio
Real number (ℝ)

Distinct 69
Distinct (%) 11.9%
Missing 4
Missing (%) 0.7%
Infinite 0
Infinite (%) 0.0%
Mean 0.9470639
Minimum 0.3
Maximum 2.8
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 4.7 KiB
2023-03-24T17:55:41.334423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum 0.3
5-th percentile 0.5
Q1 0.7
median 0.93
Q3 1.1
95-th percentile 1.5
Maximum 2.8
Range 2.5
Interquartile range (IQR) 0.4

Descriptive statistics

Standard deviation 0.31959211
Coefficient of variation (CV) 0.33745569
Kurtosis 3.2818998
Mean 0.9470639
Median Absolute Deviation (MAD) 0.17
Skewness 0.99229945
Sum 548.35
Variance 0.10213912
Monotonicity Not monotonic
2023-03-24T17:55:41.461182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
1 106
18.2%
0.8 65
11.1%
0.9 59
10.1%
0.7 53
9.1%
1.1 46
7.9%
1.2 35
 
6.0%
0.6 31
 
5.3%
0.5 29
 
5.0%
1.3 25
 
4.3%
1.4 17
 
2.9%
Other values (59) 113
19.4%
Value Count Frequency (%)
0.3 4
 
0.7%
0.35 1
 
0.2%
0.37 1
 
0.2%
0.39 1
 
0.2%
0.4 14
2.4%
0.45 1
 
0.2%
0.46 1
 
0.2%
0.47 2
 
0.3%
0.48 1
 
0.2%
0.5 29
5.0%
Value Count Frequency (%)
2.8 1
 
0.2%
2.5 2
 
0.3%
1.9 1
 
0.2%
1.85 2
 
0.3%
1.8 3
0.5%
1.72 1
 
0.2%
1.7 4
0.7%
1.66 1
 
0.2%
1.6 5
0.9%
1.58 2
 
0.3%

Dataset
Categorical

Distinct 2
Distinct (%) 0.3%
Missing 0
Missing (%) 0.0%
Memory size 4.7 KiB
1
416 
2
167 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 583
Distinct characters 2
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 1
2nd row 1
3rd row 1
4th row 1
5th row 1

Common Values

Value Count Frequency (%)
1 416
71.4%
2 167
28.6%

Length

2023-03-24T17:55:41.558669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-24T17:55:41.630183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Value Count Frequency (%)
1 416
71.4%
2 167
28.6%

Most occurring characters

Value Count Frequency (%)
1 416
71.4%
2 167
28.6%

Most occurring categories

Value Count Frequency (%)
Decimal Number 583
100.0%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
1 416
71.4%
2 167
28.6%

Most occurring scripts

Value Count Frequency (%)
Common 583
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
1 416
71.4%
2 167
28.6%

Most occurring blocks

Value Count Frequency (%)
ASCII 583
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
1 416
71.4%
2 167
28.6%

Interactions

2023-03-24T17:55:38.299294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:31.695741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:32.543886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:33.415142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:34.217410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:35.008384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:35.912534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:36.776913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:37.549739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:38.394037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:31.787320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:32.632018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:33.501814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:34.298027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:35.089704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:35.990240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:36.862057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:37.628174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:38.476143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:31.888159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:32.719037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:33.594252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:34.416425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:35.197069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:36.092513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:36.952748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:37.709972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:38.570277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:31.981991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:32.895662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:33.694273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:34.512566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:35.331373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:36.189409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:37.041522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:37.789772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:38.650987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:32.099311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:32.977388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:33.779834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:34.586468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:35.424987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:36.263516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:37.116337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:37.871982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:38.746455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:32.203481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:33.063461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:33.881521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:34.678381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:35.555654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:36.362543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:37.208122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:37.964761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:38.826061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:32.282048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:33.159297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:33.971199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:34.760448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:35.656957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:36.445465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:37.283890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:38.052368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:38.910574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:32.388657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:33.247883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:34.054210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:34.848194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:35.741037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:36.525257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:37.389576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:38.145755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:38.983645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:32.464657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:33.328493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:34.132234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:34.924620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:35.823929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:36.606341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:37.473356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-24T17:55:38.221519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-24T17:55:41.689694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Age Total_Bilirubin Direct_Bilirubin Alkaline_Phosphotase Alamine_Aminotransferase Aspartate_Aminotransferase Total_Protiens Albumin Albumin_and_Globulin_Ratio Gender Dataset
Age 1.000 0.114 0.106 0.059 -0.068 -0.018 -0.174 -0.261 -0.250 0.123 0.200
Total_Bilirubin 0.114 1.000 0.959 0.384 0.437 0.509 -0.019 -0.222 -0.285 0.076 0.187
Direct_Bilirubin 0.106 0.959 1.000 0.368 0.412 0.504 -0.020 -0.233 -0.298 0.080 0.214
Alkaline_Phosphotase 0.059 0.384 0.368 1.000 0.411 0.396 0.014 -0.171 -0.322 0.104 0.202
Alamine_Aminotransferase -0.068 0.437 0.412 0.411 1.000 0.774 -0.019 -0.053 -0.083 0.000 0.113
Aspartate_Aminotransferase -0.018 0.509 0.504 0.396 0.774 1.000 -0.085 -0.205 -0.209 0.000 0.093
Total_Protiens -0.174 -0.019 -0.020 0.014 -0.019 -0.085 1.000 0.779 0.273 0.166 0.000
Albumin -0.261 -0.222 -0.233 -0.171 -0.053 -0.205 0.779 1.000 0.754 0.042 0.155
Albumin_and_Globulin_Ratio -0.250 -0.285 -0.298 -0.322 -0.083 -0.209 0.273 0.754 1.000 0.000 0.204
Gender 0.123 0.076 0.080 0.104 0.000 0.000 0.166 0.042 0.000 1.000 0.066
Dataset 0.200 0.187 0.214 0.202 0.113 0.093 0.000 0.155 0.204 0.066 1.000

Missing values

2023-03-24T17:55:39.087157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-24T17:55:39.231301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Age Gender Total_Bilirubin Direct_Bilirubin Alkaline_Phosphotase Alamine_Aminotransferase Aspartate_Aminotransferase Total_Protiens Albumin Albumin_and_Globulin_Ratio Dataset
0 65 Female 0.7 0.1 187 16 18 6.8 3.3 0.90 1
1 62 Male 10.9 5.5 699 64 100 7.5 3.2 0.74 1
2 62 Male 7.3 4.1 490 60 68 7.0 3.3 0.89 1
3 58 Male 1.0 0.4 182 14 20 6.8 3.4 1.00 1
4 72 Male 3.9 2.0 195 27 59 7.3 2.4 0.40 1
5 46 Male 1.8 0.7 208 19 14 7.6 4.4 1.30 1
6 26 Female 0.9 0.2 154 16 12 7.0 3.5 1.00 1
7 29 Female 0.9 0.3 202 14 11 6.7 3.6 1.10 1
8 17 Male 0.9 0.3 202 22 19 7.4 4.1 1.20 2
9 55 Male 0.7 0.2 290 53 58 6.8 3.4 1.00 1
Age Gender Total_Bilirubin Direct_Bilirubin Alkaline_Phosphotase Alamine_Aminotransferase Aspartate_Aminotransferase Total_Protiens Albumin Albumin_and_Globulin_Ratio Dataset
573 32 Male 3.7 1.6 612 50 88 6.2 1.9 0.40 1
574 32 Male 12.1 6.0 515 48 92 6.6 2.4 0.50 1
575 32 Male 25.0 13.7 560 41 88 7.9 2.5 2.50 1
576 32 Male 15.0 8.2 289 58 80 5.3 2.2 0.70 1
577 32 Male 12.7 8.4 190 28 47 5.4 2.6 0.90 1
578 60 Male 0.5 0.1 500 20 34 5.9 1.6 0.37 2
579 40 Male 0.6 0.1 98 35 31 6.0 3.2 1.10 1
580 52 Male 0.8 0.2 245 48 49 6.4 3.2 1.00 1
581 31 Male 1.3 0.5 184 29 32 6.8 3.4 1.00 1
582 38 Male 1.0 0.3 216 21 24 7.3 4.4 1.50 2

Duplicate rows

Most frequently occurring

Age Gender Total_Bilirubin Direct_Bilirubin Alkaline_Phosphotase Alamine_Aminotransferase Aspartate_Aminotransferase Total_Protiens Albumin Albumin_and_Globulin_Ratio Dataset # duplicates
0 18 Male 0.8 0.2 282 72 140 5.5 2.5 0.80 1 2
1 30 Male 1.6 0.4 332 84 139 5.6 2.7 0.90 1 2
2 31 Male 0.6 0.1 175 48 34 6.0 3.7 1.60 1 2
3 34 Male 4.1 2.0 289 875 731 5.0 2.7 1.10 1 2
4 36 Male 0.8 0.2 158 29 39 6.0 2.2 0.50 2 2
5 36 Male 5.3 2.3 145 32 92 5.1 2.6 1.00 2 2
6 38 Female 2.6 1.2 410 59 57 5.6 3.0 0.80 2 2
7 39 Male 1.9 0.9 180 42 62 7.4 4.3 1.38 1 2
8 40 Female 0.9 0.3 293 232 245 6.8 3.1 0.80 1 2
9 42 Male 8.9 4.5 272 31 61 5.8 2.0 0.50 1 2